Preferred Deals in General Environments
Authors: Yuan Deng, Sébastien Lahaie, Vahab Mirrokni
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm using auction data from a major advertising exchange and our empirical results show that the algorithm achieves around 95% of the optimal revenue. |
| Researcher Affiliation | Collaboration | Yuan Deng1 , S ebastien Lahaie2 and Vahab Mirrokni2 1Duke University 2Google Research ericdy@cs.duke.edu, {slahaie, mirrokni}@google.com |
| Pseudocode | Yes | Algorithm 1: AAG Framework |
| Open Source Code | No | The paper refers to using the Glop linear programming solver and provides a link to its documentation, but does not provide access to the authors' own implementation code. |
| Open Datasets | No | We use data collected from the Google Ad Exchange (Ad X) over a period of one day in summer 2018. |
| Dataset Splits | No | The paper describes the data collection and instance creation (100K auctions) and how experiments are repeated (50 times) with varying budget ratios, but it does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | Each run of the experiment takes roughly 30 seconds on a single CPU. |
| Software Dependencies | No | The paper mentions 'Python 2.7' and 'Glop linear programming solver' but does not provide specific version numbers for the Glop solver or other required libraries. |
| Experiment Setup | Yes | We run our experiment on 5 high-volume inventory units for the day in question. ... we discretize the bids to cents and only consider the top 50 most frequent buyer-bid pairs. ... take the first 100K auctions in which at least two of the top 50 buyer-bid pairs appear in the auction to form our instances. ... We conduct experiments parametrized by a budget ratio r [0.1, 1.5]. ... For a fixed setting of r, we repeat the experiment 50 times and generate the budget as follows: for each run, (1) compute the contribution si to the social welfare for each buyer i, by summing buyer i s bids over all auctions that it wins; (2) set buyer i s budget to a value uniformly drawn from [0, 2 si r], so that the mean of the generated budget is si r, proportional to the buyer s social welfare contribution. |